Pattern recognition systems have been contemplated for many years and have gained acceptance for some applications. However, one of the major obstacles that stand in the way of wider acceptance and use is the difficulty in installing, configuring and maintaining these systems. Potential customers often elect not to implement these systems because the setup and configuration procedures are simply too complicated to be cost effective. This difficulty stems from the fundamental issue that pattern classification systems are only as accurate as the information used to set up the classifier.
Pattern classification systems are designed to match patterns of data that are acquired by sensors to an existing classification database or “training set.” The training set is programmed into the device to provide a wide variety of examples of patterns that belong to one or more object classes that are to be recognized. When a pattern of data matches the training set to within a certain accuracy the detected data is classified to belong to certain class. The ability of the pattern recognition systems to accurately classify measured data is dependent on the size and diversity of the training set. Unfortunately, while designing a classification system, it is often difficult to predict the variations of data that the system will measure. For example, actual variations in the members of the class, variations in the measurements from sensor inaccuracies, sensor noise, system setup variations, system noise, and variations in the environment or environmental noise may differ for each system installed in the field.
Due to these variations, pattern recognition systems often incorporate the ability to adapt to new classification data via supervised or unsupervised learning. This adaptive ability allows the training set to be expanded to include new data acquired after the initial installation. In addition, new training data is often extracted from these “field trained” devices and manually included in future installations of pattern recognition systems.
However, there are several fundamental problems associated with this approach. For example, if the system is static, i.e. does not use an adaptive classification algorithm with learning, it cannot adapt to actual variations associated with its local environment such as variations in the members of the class, variations in the measurements due to sensor inaccuracies, sensor noise, system setup variations, system noise, variations in the environment or environmental noise, etc.
On the other hand, if the system uses an adaptive classification algorithm that relies on unsupervised learning, the sensor designer has limited control of the end state of the classification training set. This lack of control has the undesired effect that individual sensors will perform differently under identical conditions due to the non-deterministic characteristics of learning associated with different data being “learned” by each device. Systems that rely on these unsupervised approaches also require additional computing resources and power at the device.
If the system uses an adaptive algorithm that relies only on supervised learning, the designer or installer is forced to supervise the training of each device in the field to adapt to the new conditions. Thus, the installer must simulate as many variations in the classification members and environmental variations as possible to train the system. This approach is often impractical and validates customers' complaints concerning the complexity of the system.
To overcome many of the above deficiencies, system designers often attempt to minimize variations by specifying high quality components which increases the cost of the system. For example, high quality sensors minimize sensor bias and noise; expensive hardware filters minimize sensor, system and environmental noise; high speed processors may implement complex software filters, and execute feature extraction and complex classification algorithms; and large amounts of system memory may store a large training set, allowing for as many anticipated variations in the actual class members as possible, as well as variations in environmental conditions.
Additionally, the system is usually equipped with high bandwidth data port connections to allow installers to monitor sensor data directly during installation and to assist in the supervised training of the devices. In the event that the environmental conditions change, the system performance will often be affected, causing the installer to retune the system.
If the end-customer requests a change to the system's operation, such as recognition of a new class of objects or data, the designer must create a new classification training set and installer must repeat the installation procedure to tune the system with the new class members.
Therefore, what is needed is a system and method for collecting and compiling pattern recognition data from multiple local image processing systems such that the collected data can be used to update the local processing system to allow for changes in the environment and to configure and update additional image processing systems.